import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import seaborn as sns

import numpy as np
import pandas as pd
import os
import sys

# 获取当前脚本的完整路径
script_path = os.path.abspath(sys.argv[0])
# 从完整路径中获取目录
script_dir = os.path.dirname(script_path)
# 从完整路径中分离出文件名
script_name = os.path.basename(script_path)
# 使用 splitext() 函数分离文件名和扩展名
script_name_without_extension, _ = os.path.splitext(script_name)

# 创建保存图像的完整路径
save_path = os.path.join(script_dir, script_name_without_extension + ".png")


# Prepare Data
df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv", parse_dates=['date']).head(100)
x = np.arange(df.shape[0])
y_returns = (df.psavert.diff().fillna(0)/df.psavert.shift(1)).fillna(0) * 100

# Plot
plt.figure(figsize=(16,10), dpi= 80)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] >= 0, facecolor='green', interpolate=True, alpha=0.7)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] <= 0, facecolor='red', interpolate=True, alpha=0.7)

# Annotate
plt.annotate('Peak 1975', xy=(94.0, 21.0), xytext=(88.0, 28),
             bbox=dict(boxstyle='square', fc='firebrick'),
             arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white')


# Decorations
xtickvals = [str(m)[:3].upper()+"-"+str(y) for y,m in zip(df.date.dt.year, df.date.dt.month_name())]
plt.gca().set_xticks(x[::6])
plt.gca().set_xticklabels(xtickvals[::6], rotation=90, fontdict={'horizontalalignment': 'center', 'verticalalignment': 'center_baseline'})
plt.ylim(-35,35)
plt.xlim(1,100)
plt.title("Month Economics Return %", fontsize=22)
plt.ylabel('Monthly returns %')
plt.grid(alpha=0.5)
plt.savefig(save_path, dpi=300)
plt.show()